U.S. patent application number 16/413998 was filed with the patent office on 2019-11-21 for food safety risk and sanitation compliance tracking.
The applicant listed for this patent is Ecolab USA Inc.. Invention is credited to Nicolas A. Granucci, Gregory B. Hayes, Adam T. Johnson, Kevin S. Smyth, Jeffrey L. Testa, Tracy A. Thomas, Darrell B. Wiser.
Application Number | 20190354907 16/413998 |
Document ID | / |
Family ID | 66691042 |
Filed Date | 2019-11-21 |
![](/patent/app/20190354907/US20190354907A1-20191121-D00000.png)
![](/patent/app/20190354907/US20190354907A1-20191121-D00001.png)
![](/patent/app/20190354907/US20190354907A1-20191121-D00002.png)
![](/patent/app/20190354907/US20190354907A1-20191121-D00003.png)
![](/patent/app/20190354907/US20190354907A1-20191121-D00004.png)
![](/patent/app/20190354907/US20190354907A1-20191121-D00005.png)
![](/patent/app/20190354907/US20190354907A1-20191121-D00006.png)
![](/patent/app/20190354907/US20190354907A1-20191121-D00007.png)
United States Patent
Application |
20190354907 |
Kind Code |
A1 |
Granucci; Nicolas A. ; et
al. |
November 21, 2019 |
FOOD SAFETY RISK AND SANITATION COMPLIANCE TRACKING
Abstract
A system includes a server connected to a network. The server
includes a data collection module, a database interaction module,
and a predictive analysis module. The data collection module is
configured to collect data, via the network, from one or more data
sources, the data being related to food safety risk and sanitation
compliance tracking of a food establishment. The database
interaction module is configured to store, into a database, data
collected by the data collection module and to retrieve data from
the database. The predictive analysis module is configured to
analyze data in the database and calculate, based on the analyzed
data, a probability of the food establishment violating a health
code.
Inventors: |
Granucci; Nicolas A.;
(Woodbury, MN) ; Testa; Jeffrey L.; (Greensboro,
NC) ; Smyth; Kevin S.; (Woodbury, MN) ;
Johnson; Adam T.; (Bentonville, AR) ; Thomas; Tracy
A.; (Woodbury, MN) ; Wiser; Darrell B.; (Lehi,
UT) ; Hayes; Gregory B.; (Apple Valley, MN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Ecolab USA Inc. |
St. Paul |
MN |
US |
|
|
Family ID: |
66691042 |
Appl. No.: |
16/413998 |
Filed: |
May 16, 2019 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
62672944 |
May 17, 2018 |
|
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 9/542 20130101;
G06Q 10/0635 20130101; G06Q 30/018 20130101; G06Q 10/087 20130101;
G06Q 50/12 20130101; G06Q 10/06 20130101 |
International
Class: |
G06Q 10/06 20060101
G06Q010/06; G06Q 30/00 20060101 G06Q030/00; G06F 9/54 20060101
G06F009/54 |
Claims
1. A system, comprising: a server connected to a network, the
server comprising: a data collection module configured to collect
data, via the network, from one or more data sources, the data
being related to food safety risk and sanitation compliance
tracking of a food establishment; a database interaction module
configured to store, into a database, data collected by the data
collection module and to retrieve data from the database; and a
predictive analysis module configured to analyze data in the
database and calculate, based on the analyzed data, a probability
of the food establishment violating a health code.
2. The system of claim 1, wherein the one or more data sources
include at least one of: sensor data from one or more sensors of
the food establishment, dispenser data from one or more dispensers
of the food establishment, pest control data, health department
inspection data, self-audit data, self-reported data, and equipment
care and maintenance data.
3. The system of claim 2, wherein the one or more sensors of the
food establishment include at least one of: thermometers,
hygrometers, and barometers.
4. The system of claim 2, wherein the one or more dispensers
include at least one of: sanitizer dispensers and water
dispensers.
5. The system of claim 1, wherein upon the calculated probability
of the food establishment violating a health code exceeding a
threshold, the server transmits a notification to one or more
devices associated with the food establishment.
6. The system of claim 1, wherein the server further comprises: a
report generator module configured to generate a report including
the probability of the food establishment violating the health
code.
7. The system of claim 6, wherein the report generator is
configured to transmit the report to a client device for
display.
8. The system of claim 6, wherein the probability of the food
establishment violating the health code comprises a predictive risk
score for the food establishment.
9. The system of claim 8, wherein the probability of the food
establishment violating the health code comprises a plurality of
individual risk indicators for the food establishment, and wherein
each of the plurality of individual risk indicators for the food
establishment provides an assessment of risk relative to other food
establishments.
10. The system of claim 9, wherein the plurality of individual risk
indicators for the food establishment comprise personal hygiene,
cleaning and sanitation, time and temperature, and
documentation.
11. A method implemented on at least one server connected to a
network, the method comprising: collecting data, via the network,
from one or more data sources, the data being related to food
safety risk and sanitation compliance tracking of a food
establishment; storing, into a database, the collected data;
retrieving data from the database; analyzing data in the database;
and calculating, based on the analyzed data, a probability of the
food establishment violating a health code.
12. The method of claim 11, wherein the one or more data sources
include at least one of: sensor data from one or more sensors of
the food establishment, dispenser data from one or more dispensers
of the food establishment, pest control data, health department
inspection data, self-audit data, self-reported data, and equipment
care and maintenance data.
13. The method of claim 12, wherein the one or more sensors of the
food establishment include at least one of: thermometers,
hygrometers, and barometers.
14. The method of claim 12, wherein the one or more dispensers
include at least one of: sanitizer dispensers and water
dispensers.
15. The method of claim 11, further comprising: transmitting a
notification to one or more devices associated with the food
establishment upon the calculated probability of the food
establishment violating a health code exceeding a threshold.
16. A non-transitory computer-readable medium including
instructions that, when executed by a computer, cause the computer
to: collect data, via a network, from one or more data sources, the
data being related to food safety risk and sanitation compliance
tracking of a food establishment; store, into a database, the
collected data; retrieve data from the database; analyze data in
the database; and calculate, based on the analyzed data, a
probability of the food establishment violating a health code.
17. The non-transitory computer-readable medium of claim 16,
wherein the one or more data sources include at least one of:
sensor data from one or more sensors of the food establishment,
dispenser data from one or more dispensers of the food
establishment, pest control data, health department inspection
data, self-audit data, self-reported data, and equipment care and
maintenance data.
18. The non-transitory computer-readable medium of claim 17,
wherein the one or more sensors of the food establishment include
at least one of: thermometers, hygrometers, and barometers.
19. The non-transitory computer-readable medium of claim 17,
wherein the one or more dispensers include at least one of:
sanitizer dispensers and water dispensers.
20. The non-transitory computer-readable medium of claim 16,
further comprising instructions that, when executed by a computer,
cause the computer to: transmit a notification to one or more
devices associated with the food establishment upon the calculated
probability of the food establishment violating a health code
exceeding a threshold.
Description
RELATED MATTERS
[0001] This application claims priority to U.S. Patent Application
No. 62/672,944, filed May 17, 2018, the entire contents of which
are incorporated herein by reference.
TECHNICAL FIELD
[0002] This disclosure relates generally to food safety and
sanitation risk and compliance systems and methods.
SUMMARY
[0003] Embodiments disclosed herein can analyze data from a variety
of sources and output information that can provide insights into
specific risk factors in a way that can facilitate targeted action
to address such risk factors. As such, embodiments disclosed herein
can allow a user to proactively reduce risks relating to food
safety and sanitation. Furthermore, embodiments disclosed herein
can allow a user to allocate finite resources to certain risk
factors specific to that user that are most likely to result in the
highest reduction in overall food safety and sanitation risk.
[0004] One exemplary embodiment includes a food safety risk system.
This system embodiment includes a server connected to a network.
The server includes a data collection module, a database
interaction module, and a predictive analysis module. The data
collection module is configured to collect data, via the network,
from one or more data sources, the data being related to food
safety risk and sanitation compliance tracking of a food
establishment. The database interaction module is configured to
store, into a database, data collected by the data collection
module and to retrieve data from the database. The predictive
analysis module is configured to analyze data in the database and
calculate, based on the analyzed data, a probability of the food
establishment violating a health code.
[0005] In a further embodiment, the server also includes a report
generation module. The report generation module is configured to
generate a report including the probability of the food
establishment violating the health code. In some such examples, the
report generator is configured to transmit the report to a client
device for display.
[0006] In the above system embodiments, the probability of the food
establishment violating the health code can include a predictive
risk score for the food establishment. For example, the probability
of the food establishment violating the health code can include the
predictive risk score for the food establishment and a plurality of
individual risk indicators for the food establishment. Each of the
plurality of individual risk indicators for the food establishment
can provide an assessment of risk relative to other food
establishments. Examples that can be included as the plurality of
individual risk indicators for the food establishment include
personal hygiene, cleaning and sanitation, time and temperature,
and documentation. Further examples that can be included as the
plurality of individual risk indicators for the food establishment
include cross-contamination, pest control, date marking, and other,
or miscellaneous, data.
[0007] Another exemplary embodiment includes a method implemented
on at least one server connected to a network. This method
embodiment can include the step of collecting data, via the
network, from one or more data sources, the data being related to
food safety risk and sanitation compliance tracking of a food
establishment. This method can further include the steps of
storing, into a database, the collected data, retrieving data from
the database, analyzing data in the database, and calculating,
based on the analyzed data, a probability of the food establishment
violating a health code.
[0008] In a further embodiment, the method can include a step of
generating a report including the probability of the food
establishment violating the health code. This further embodiment
may also include the step of transmitting the report to a client
device for display.
[0009] In the above method embodiments, the probability of the food
establishment violating the health code can include a predictive
risk score for the food establishment. For example, the probability
of the food establishment violating the health code can include the
predictive risk score for the food establishment and a plurality of
individual risk indicators for the food establishment. Each of the
plurality of individual risk indicators for the food establishment
can provide an assessment of risk relative to other food
establishments. Examples that can be included as the plurality of
individual risk indicators for the food establishment include
personal hygiene, cleaning and sanitation, time and temperature,
and documentation. Further examples that can be included as the
plurality of individual risk indicators for the food establishment
include cross-contamination, pest control, date marking, and other,
or miscellaneous, data.
[0010] A further embodiment includes a non-transitory
computer-readable medium including instructions. When executed by a
computer, these instructions cause the computer to collect data,
via a network, from one or more data sources, the data being
related to food safety risk and sanitation compliance tracking of a
food establishment. When executed by a computer, these instructions
can further cause the computer to store, into a database, the
collected data, retrieve data from the database, analyze data in
the database, and calculate, based on the analyzed data, a
probability of the food establishment violating a health code.
[0011] In a further embodiment, when executed by a computer, the
above instructions can further cause the computer to generate a
report including the probability of the food establishment
violating the health code. These instructions, when executed by a
computer, may also cause the computer to transmit the report to a
client device for display.
[0012] In the above embodiments of the non-transitory
computer-readable medium including instructions, the instructions,
when executed by a computer, can cause the computer to calculate
the probability of the food establishment violating the health code
to include a predictive risk score for the food establishment. For
example, the probability of the food establishment violating the
health code can be calculated to include the predictive risk score
for the food establishment and a plurality of individual risk
indicators for the food establishment. Each of the plurality of
individual risk indicators for the food establishment can be
calculated to provide an assessment of risk relative to other food
establishments. Examples that can be calculated as the plurality of
individual risk indicators for the food establishment include
personal hygiene, cleaning and sanitation, time and temperature,
and documentation. Further examples that can be included as the
plurality of individual risk indicators for the food establishment
include cross-contamination, pest control, date marking, and other,
or miscellaneous, data.
BRIEF DESCRIPTION OF THE DRAWINGS
[0013] The following drawings are illustrative of particular
embodiments of the present invention and therefore do not limit the
scope of the invention. The drawings are intended for use in
conjunction with the explanations in the following description.
Embodiments of the invention will hereinafter be described in
conjunction with the appended drawings, wherein like numerals
denote like elements.
[0014] FIG. 1 illustrates a system for food safety risk and
sanitation compliance tracking, according to an example
embodiment.
[0015] FIG. 2 illustrates various modules that may be executed by a
food safety risk and sanitation compliance tracking system,
according to an example embodiment.
[0016] FIG. 3 illustrates a flow of information through the food
safety risk and sanitation compliance tracking system, according to
an example embodiment.
[0017] FIG. 4A illustrates a report for a food establishment,
according to an example embodiment.
[0018] FIGS. 4B and 4C illustrate another report for a food
establishment, according to an example embodiment. FIG. 4B shows an
overall customer report, while FIG. 4C shows certain aspects of the
report for a selected customer store.
[0019] FIG. 5 is a block diagram illustrating an example of a
machine, upon which any one or more example embodiments may be
implemented.
DETAILED DESCRIPTION
[0020] The following detailed description is exemplary in nature
and is not intended to limit the scope, applicability, or
configuration of the invention in any way. Rather, the following
description provides some practical illustrations for implementing
exemplary embodiments of the present invention. Examples of
constructions, materials, and/or dimensions are provided for
selected elements. Those skilled in the art will recognize that
many of the noted examples have a variety of suitable
alternatives.
[0021] Currently, many food establishments (e.g., restaurants, meat
processing plants, grocery stores, etc.) use manual processes for
tracking their compliance with various health regulations at the
county, state, and federal levels. For example, some food
establishments undergo a periodic (e.g., monthly) self-audit,
during which one or more persons checks the food establishment for
health regulation compliance. Upon completion of the self-audit,
the managers of the food establishment may receive a compliance
"score" and/or an audit report that details the conditions of the
food establishment and how those conditions pass or fail the
associated health regulations.
[0022] The "self-audits" are a series of questions that may relate
to one or more departments/areas in a food establishment. Each
question may also be associated with one or more sections of one or
more health regulations relevant to the question. The answer to
each question may result in zero or more "findings." In an
embodiment, the number of findings possible for a given question
has no upper bound. A finding may be one of two types: a "critical"
finding, which represents a health code violation that is
sufficiently severe to warrant closing the food establishment, and
a "recommended practice" finding, which represents a health code
violation that does not warrant closing the food establishment, but
which the food establishment must correct.
[0023] Although these "self-audits" may provide useful insight into
health compliance problems that have occurred in the past, they do
little to help a food establishment to predict the probability of
future violations of health codes. However, analyzing data
integrated from 1) data from the self-audits, 2) data from health
department inspections, and 3) data from sensors of the environment
of the food establishment, may be used to calculate a probability
of the food establishment violating health codes in the future.
[0024] A comprehensive food safety and sanitation risk and
compliance system is disclosed. In an embodiment, data from various
sources is integrated into a database (or data store). The sources
of data may include one or more of the following: self-audits
(e.g., performed by a food establishment itself or by a third
party), pest elimination services, health department inspections,
dispensing equipment that monitors sanitation compliance, and
various sensors within and/or near the food establishment. The data
is analyzed to calculate a probability of a food establishment
violating health codes in the future. Various preventative measures
may then be performed in response to the calculated
probability.
[0025] Components of the system may include:
[0026] 1) a customer self-audit/data gathering and task management
tool,
[0027] 2) a data warehouse integrating data feeds from diverse
systems (e.g., a customer self-audit utility, pest elimination
service data, food safety audit data, health department inspection
data, and dispensing equipment monitoring sanitation
compliance),
[0028] 3) an alerting system that issues alerts when certain risk
factors are identified,
[0029] 4) a client portal to report data, insights and
training,
[0030] 5) a mobile app,
[0031] 6) sensors to detect various operating/environmental
conditions, and
[0032] 5) an analysis module including predictive analytics
algorithms.
[0033] These solutions combined will increase the insights into
risk factors and root causes of foodborne illness vectors, and
facilitate customers' ability to reduce such risks in a more
proactive way. This platform may be customized for multiple food
safety market segments. Furthermore, this platform may also be
customized to any enterprise that requires people in a multitude of
remote sites to collect data and utilize analysis from these
diverse data sources.
[0034] FIG. 1 illustrates a system 100 for food safety risk and
sanitation compliance tracking, according to an example embodiment.
The system may include a food establishment, one or more servers,
one or more data stores, one or more client devices, and one or
more interconnected networks (e.g., the Internet).
[0035] In an embodiment, the food establishment may be a retail
store, a quick serve restaurant, a restaurant, a deli, a bakery,
etc. The food establishment may include one or more sensors, and
one or more dispensers (e.g., chemical dispensers). The one or more
sensors may include one or more of thermometers, hygrometers,
barometers, etc. The one or more sensors may track and report one
or more physical, chemical, and/or environmental conditions, such
as temperature, pressure, humidity, etc. The one or more dispensers
may track and report dispensing events, which may include the
matter dispensed (e.g., a liquid chemical compound, baking soda,
water, etc.), the quantity dispensed, and a timestamp of the
dispensing event. The system may use data from the one or more
dispensers to identify areas of risk and/or overuse, track labor
and utility usage, and to identify and alert when critical issues
occur and/or are likely to occur.
[0036] In an embodiment, the one or more data stores may store data
from one or more sources, such as pest control data, health
department inspection data, self-audit data, self-reported data,
equipment care and maintenance data, etc. Incorporating multiple
sources of data helps to improve the system's predictive ability
and its outcome validation.
[0037] In an embodiment, the mobile app allows the user to enter
various types of data (e.g., answers to self-audit questions). For
example, the mobile app can generate and present a list of task
items to be checked, such as via user input at the mobile app, in
order to guide the completion of the self-audit (e.g., by the food
establishment itself or by a third party). In an embodiment, the
mobile app displays one or more of: data produced by one or more
sensors of the food establishment, data produced by one or more
self-audits and/or health department inspections, and results of
analyzing the data in the database pertaining to the food
establishment. In an embodiment, the client portal is integrated
into the mobile app. In an embodiment, the customer self-audit/data
gathering and task management tool is integrated into the mobile
app.
[0038] In an embodiment, the system may be used to determine if the
automatically collected data differs from the manually collected
data. For example, if the automatically collected data
significantly differs from the manually collected (e.g., self-audit
or health department inspection) data, the system may send one or
more alerts and/or notifications to inform one or more individuals
that there may be a problem with the food establishment's sensors
and/or dispensers.
[0039] FIG. 2 illustrates various modules that may be executed by a
food safety risk and sanitation compliance tracking system, for
instance at a server 200 of the system 100, according to an example
embodiment. For example, the one or more servers may execute one or
more of the following:
[0040] a data collection module, which may accept data from one or
more sources of data;
[0041] a database interaction module, which may store into a
database data collected by the data collection module and retrieve
data from the database as necessary;
[0042] a predictive analysis module, which may analyze data in the
database and calculate predictions based on the analyzed data;
[0043] a report generation module, which may generate reports based
on data in the database and/or results of the analysis module;
[0044] a client portal module, which may display a personalized
portal to each respective food establishment;
[0045] a dashboard module, which may display a personalized
dashboard for each respective food establishment; and
[0046] various other back-end or server modules to enable the
system to function as described.
[0047] FIG. 3 illustrates a flow of information 300 through the
food safety risk and sanitation compliance tracking system 100,
according to an example embodiment. Various data streams (e.g.,
self-audit data, data from outside sources (e.g., health department
data), resource and equipment data (e.g., from sensors and
dispensers), and customer-supplied data is analyzed by the
predictive analysis module. The predictive analysis module
identifies trends and predictive indicators. The trends and
predictive indicators are used to establish an action plan, which
is communicated to various devices. The devices (and/or individuals
using the devices) implement the actions in the action plan, and
the system tracks the implemented action plan for performance.
[0048] In an embodiment, the predictive analysis module assumes a
correlation (e.g., a pseudo-linear relationship) between the
quantity of findings resulting from a health department inspection
and the probability that at least one of the findings is a
"critical finding." Because of this, the predictive analysis module
may use Bayesian algorithms to calculate probabilities of health
code violations.
[0049] As previously stated, each question of a self-audit may be
associated with one or more sections of one or more health
regulations relevant to the question. In an embodiment, there can
be only one unique finding per combination of self-audit, question
of the self-audit, and area of the food establishment. For example,
a self-audit question relates to personal hygiene, and relates to 4
of 5 departments in a store; thus, during any given audit of this
store, there are 4 opportunities for this question to result in a
finding. If only 1 finding for this question is documented, then
there is a 25% (1/4) probability that the question will result in a
finding for that store.
[0050] Continuing with this example, if there are 5 self-audit
questions that relate to personal hygiene, and each of the 5
questions may apply to one or more departments of the store,
rolling up the 5 questions would result in 20 (4
departments.times.5 questions) opportunities for a finding. If
there is only 1 finding documented, then there is a 5% ( 1/20)
probability of a finding.
[0051] In an embodiment, the system gathers data for all
self-audits and health inspections of a food establishment, and
compares the self-audit data with the health inspection outcomes.
Using this analysis, the predictive analysis module may calculate,
for the food establishment in question, the probability of a future
inspection resulting in one or more findings.
[0052] The predictive analysis module may use a classifier to
predict probabilities of future health code violations. To train
the classification model, health department inspection data is
aggregated and input into the classification model. When the
classification model is used to predict probabilities of future
health code violations, current data from the various data streams
is input into the classification model. In an embodiment, a food
establishment's risk factors are weighted (e.g., risk factors A, B,
and C are inconsequential individually, but together, they signal a
significant food safety risk.)
[0053] Data produced by health department inspections ("HDI") may
include (for each inspection): the date(s) of the inspection, the
name(s) of the inspector(s), the beginning and ending time(s) of
the inspection(s), the name of the food establishment inspected,
geographical coordinates (e.g., latitude and longitude) of the food
establishment inspected, a quantity of critical findings resulting
from the inspection, a quantity of recommended practice findings,
one or more sections of a health code related to a finding, etc. In
an embodiment, sanitation compliance data is integrated into the
database. The sanitation compliance data may be generated from
various sources, including chemical dispensers that communicate
data related to dispensing events (e.g., type of dispensing event
and timestamp). In an embodiment, the chemical dispensers may
communicate wirelessly. In an embodiment, the system executes an
algorithm that interprets the event data from the dispensers to
determine compliance insights (e.g., store #1234 sanitized its
floor only 20 days of the last 30 days).
[0054] Data from sensors and dispensers may vary in importance. For
example, if a coffee pot's thermometer measures a temperature that
is too low, the coffee in the pot may spoil more quickly than if
the temperature was within an acceptable temperature range;
however, if a thermometer that measures rotisserie chickens
indicates that the temperatures of a cooked rotisserie chicken is
too low, the bacteria within the rotisserie chicken may not have
been adequately destroyed, resulting in a possible food safety risk
for a customer of the food establishment.
[0055] In an embodiment, some individuals (e.g., field
representatives of a service company) have access to the system.
For each food establishment, the system may inform one or more
individuals of the food establishment's current risk score, the
conditions giving rise to the current risk score, and a list of
issues that, if addressed, would result in the largest reduction to
the food establishment's current risk score.
[0056] A report can be generated that provides risk factor
information relating to one or more customer stores. FIG. 4A shows
one embodiment of such a report. FIGS. 4B and 4C show another
embodiment of such a report, where FIG. 4B illustrates an overall
customer report and FIG. 4C illustrates certain aspects of that
customer report for a selected customer store. For instance, the
food safety risk and sanitation compliance tracking system 100 can
generate and display such reports at, for instance, one or more
client devices. In one example, the predictive analysis module at
the server can analyze data input into the database and calculate,
based on the analyzed data, a probability of a store (e.g., food
establishment) violating a health code, and the report generator
module at the server can generate the report for display at one or
more client devices. For instance, in this example, the predictive
analysis module at the server can analyze data input into the
database and calculate a predictive risk score and individual risk
indicators for each of one or more stores, and the report generator
module at the server can generate the report with the calculated
predictive risk score and individual risk indicators for each of
one or more stores for display at one or more client devices.
[0057] FIG. 4A illustrates a report 400 for a food establishment,
according to an example embodiment. A food establishment's report
may include an indication whether data from one or more data
streams is trending positively or negatively. The report may
include an indication whether a food establishment's risk is
increasing, decreasing, or unchanged. In the embodiment illustrated
in FIG. 4A, information for store #304575 is displayed. The
"Current High Risk Scores" section of the report lists store
numbers, the current risk score for each store number, and an
indication whether the current risk score has increased, decreased,
or remained unchanged from the previous report. The stores listed
in a selected store's report may be associated with the selected
store by geography, by organizational structure, or by some other
association.
[0058] In an embodiment, a food establishment's report may indicate
the food establishment's performance in one or more categories of a
health code. In an embodiment, a food establishment's report may
indicate the food establishment's performance in one or more
categories of a health code relative to other food establishments
belonging to the same organizational entity (e.g., brand, chain,
company, division, etc.) as the food establishment.
[0059] In the embodiment illustrated in FIG. 4A, the food
establishment's relative performance in each category (relative to
other food establishments) is illustrated as an arc. The length of
an arc is inversely proportional to the food establishment's
relative performance in that category. In an embodiment, an arc's
color may indicate the food establishment's relative performance in
that category.
[0060] In an embodiment, training programs are tailored to the risk
factors identified in a food establishment's report. The training
programs may be delivered to employees of the food establishment
via its client portal. In an embodiment, links to training programs
may be delivered to employees of the food establishment upon a
trigger condition being detected (e.g., floor not cleaned at the
end of the day).
[0061] FIGS. 4B and 4C show a report 410 for a food establishment,
according to another example embodiment. As noted, the food safety
risk and sanitation compliance tracking system 100 can generate and
display the report 410 at, for instance, one or more client
devices.
[0062] The report 410 includes a store selection panel 415. The
store selection panel 415 can receive user input specifying one or
more specific customer stores, and the report 410 can generate
other information shown in the report 410 according to the
specified one or more customer stores (e.g. food establihsments) at
the store selection panel 415. In the illustrated example, "all"
stores are selected at the store selection panel 415. As such, the
server of the system 100 can retrieve input information from the
data store relating to the selected one or more stores at the store
selection panel 415, process this specified information, and
generate the report 410.
[0063] The report 410 also includes a risk category display 420.
The risk category display 420 provides a breakdown of customer
stores according to the predictive risk category of these stores.
As shown in the example here, the risk category display 420 breaks
the customers stores into three categories that signify low,
moderate, and high predictive risk. Any category of the risk
category display 420 can be selected by a user and, upon such
selection, the customer stores in the selected category can be
displayed in the report 410. As such, the risk category display 420
can allow a user to view a subset of customer stores in isolation.
For instance, a user may select the high category predictive risk
stores from the risk category display 420 and the report can then
display detailed information for the customer stores in the high
category predictive risk. This may allow a user to selectively
assess detailed information relating to an individual category of
stores and devote finite resources to addressing risk in this
category of stores.
[0064] The report 410 further includes a store risk panel 425. The
store risk panel 425 can list specific customer stores 426 and, for
each specific customer store, a predictive risk score 427 and a
risk change indicator 428. The predictive risk score 427 can be
computed based on various data input into the system, as described
elsewhere herein, and can represent a probability of the store
(e.g., food establishment) violating a health code. The predictive
risk score can represent the relative likelihood that the
associated customer store will have more than a predetermined
number of health inspection findings, if such inspection were to be
currently undertaken. As such, the higher the predictive risk
score, the higher the likelihood that the associated customer store
would currently have more than a predetermined number of health
inspection findings. The risk change indicator can represent a
change in the predictive risk score. The risk change indicator can
designate (e.g., using an up/down, sideways arrow and/or red,
green, or neutral color) whether the predictive risk score for the
associated customer store has increased, decreased, or remained
constant over a preset past period of time (e.g., since the last
report was run, since the last inspection, over the last month,
quarter, etc.). In the example shown here, the report 410 can also
include a total number of customer stores 429 that have had an
increase in the predictive risk score, a decrease in the predictive
risk score, and that have had no change in the predictive risk
score over the preset past period of time.
[0065] The report 410, as shown here, can additionally include a
risk indicator panel 430. The risk indicator panel can include a
number of individual risk indicators 435. Each risk indicator 435
can provide an assessment of performance in a specified food risk
category for a customer store (e.g., food establishment), or
grouping of customer stores if so selected. In some cases, each
individual risk indicator 435 can represent a probability of a
store (e.g., food establishment) violating a health code in the
corresponding category for that individual risk indicator 435. In
the illustrated example, there are risk indicators 435 for each of
personal hygiene, cross-contamination, cleaning and sanitation,
time and temperature, pest control, date marking, documentation,
and other. Also in the illustrated embodiment, each risk indicator
435 can display the performance of a customer store, or grouping of
customer stores if so selected, in the individual risk indicators,
for instance relative to other, non-selected customer stores or
relative to a predefined standard for each risk indicator. Here,
each risk indicator 435 is represented by an arc and accompanying a
gauge, with the gauge placed at a location on the arc according to
the performance for the specified risk indicator. The act can
include distinguishable (e.g., color-coded, pattern-coded) portions
along it, with each distinguishable portion corresponding to a
different level of risk of incurring a finding during a health
inspection of the store(s).
[0066] The report 410 also includes an action portion 440. The
action portion 440 can specify particular action items that a user
can take to reduce the predictive risk score 428 for a specified
customer store, or grouping of specified customer stores. For
instance, if the personal hygiene risk indicator 435 is relatively
high, the action portion 440 may specify particular action items
(e.g., sensors indicate hand washing soap dispenser is being used
five times an hour, but use should be increased to ten times an
hour based on the number of personnel working at the store; train
employees on hand washing procedure and frequency; increase the
frequency of self-audits) that can be taken at the selected
customer store(s) to reduce the personal hygiene risk indicator
435, and ultimately reduce the overall predictive risk score 428.
The report 410 can generate particular action items for display in
the action portion 440 based on instructions stored within the
system described herein. Such instructions can, for example,
include specified actions associated with each risk indicator in
the report. In one embodiment, the action portion can include a
portion with one or more particular action items directed to the
food establishment and another portion with one or more particular
action items directed to someone other than the food establishment
(e.g., a third party service provider).
[0067] In some cases, it can be useful for the report 410 to
include a geographic display 450. The geographic display 450 can
show the location of customer stores along with a relative
indication (e.g., color, shape, etc.) of each displayed store's
predictive risk. This can allow the report to convey whether stores
in a particular risk category (e.g., high risk) are geographically
concentrated, which may be useful in assessing remedies to reduce
risk factors for such stores.
[0068] FIG. 4C shows the report 410 generated for a specific store
(e.g., food establishment) 426 selected by a user from the store
risk panel 425. Once selected, the specific store 426 may be
displayed in isolation within the store risk panel 425. As shown
here, the specific selected store 426 has the risk change indicator
428 showing that the predictive risk score 427 has increased over
the past predetermined period of time. The display of the total
number of customer stores 429 can also be isolated to the specific
selected store 426. In other examples, two or more stores can be
selected by a user from the store risk panel 425 and the specific
selected two more stores can be displayed as described herein.
[0069] Selection of the specific store 426 can cause the report 410
to generate the risk indicator panel 430 for the specific store
426. In this example, the risk indicator panel 430 includes
individual risk indicators 435 each showing relative performance of
the specific store 426 in the associated category relative to
other, non-selected stores. As seen here, the risk indicator panel
430 shows that the specific store 426 is performing at a high level
of risk (e.g., risk of a finding during a health inspection),
relative to non-selected stores, in the indicator categories of
time and temperature, date marking, documentation, and other. As
such, the risk indicator panel 430 for the specific store 426 can
indicate to a user that these categories can be addressed to lower
the predictive risk score 427 for the specific store 426.
[0070] In addition, selection of the specific store 426 can cause
the report 410 to generate the action portion 440 for the specific
store 426. In one example, the action portion 440 can display
suggested action items for those indicator categories that will
result in the greatest reduction to the predictive risk score 427.
For instance, for the specific store 426, the action portion 440
can display suggested action items for indicator categories of time
and temperature, date marking, documentation, and other--those
indicator categories in which the specific store 426 is performing
at a high level of risk (e.g., risk of a finding during a health
inspection), relative to non-selected stores.
[0071] The predictive risk score in FIGS. 4A-4C is shown as a
numerical value. In some examples, this numerical value can be an
absolute value indicative of the likelihood that a predetermined
number of findings will occur if a health inspection were to take
place at the current time. For instance, in FIG. 4A the predictive
score is shown as a percentage representing the estimated
probability that a predetermined number of findings (e.g., tow,
three, four, five, etc.) will occur if a health inspection were to
take place at the current time.
[0072] The inclusion of the predictive risk score 427 and the risk
indicator panel 430 can allow a user to ascertain those stores and
risk indicator categories, respectively, that can be addressed via
action items (e.g., shown in the action portion 440) to see the
greatest reduction in risk of an adverse health inspection
finding.
[0073] A variety of techniques can be used by the system to process
the input data and output a predictive risk score and relative risk
assessment for the various risk indicator categories. As one
example, a number of models can be generated and run to simulate
various outcomes based on the input data. For example, for each
risk indicator category, a number of models can be run using the
input data relating to that risk indicator category to simulate
outcomes in that risk indicator category. These results can be
aggregated (e.g., averaged) to arrive at the result for each risk
indicator category, and then this result can be compared to the
same in that risk indicator category for all other, non-selected
stores to display the risk indicator for that category. Similarly,
the predictive risk score for a store can be generated by
aggregating the risk indicator categories for that store. For
example, the risk indicator categories for a store can be averaged
to provide the predictive risk score for that store. In some cases,
risk indicator categories can be weighted in calculating this
average where certain applications of the system are believed to
include certain risk indicator categories that are more likely to
lead to a higher risk profile for the store than other risk
indicator categories.
[0074] In addition to the input data described previously herein,
data relating to attributes of a store's location can be used as
input data in the system. For instance, in some examples data input
into the system for generating risk indicator categories and
predictive risk scores can include data such as one or more of
population in a vicinity of the location, income in the vicinity of
the location, and tourist traffic in the vicinity.
[0075] As noted previously, the system can send alerts to a user.
For example, the system's data store may include a listing of
contact information associated with stores. In some cases, when a
predictive risk score and/or one or more individual risk indicators
changes (e.g., increases) to a predefined extent, the system can
output an alert according to the associated contact information
associated with the store(s).
[0076] FIG. 5 is a block diagram illustrating an example of a
machine 500, upon which any one or more example embodiments may be
implemented. In alternative embodiments, the machine 500 may
operate as a standalone device or may be connected (e.g.,
networked) to other machines. In a networked deployment, the
machine 500 may operate in the capacity of a server machine, a
client machine, or both in a client-server network environment. In
an example, the machine 500 may act as a peer machine in a
peer-to-peer (P2P) (or other distributed) network environment. The
machine 500 may implement or include any portion of the systems,
devices, or methods illustrated in FIGS. 1-4, and may be a
computer, a server, or any machine capable of executing
instructions (sequential or otherwise) that specify actions to be
taken by that machine. Further, although only a single machine is
illustrated, the term "machine" shall also be taken to include any
collection of machines that individually or jointly execute a set
(or multiple sets) of instructions to perform any one or more of
the methodologies discussed herein, such as cloud-based computing,
software as a service (SaaS), other computer cluster
configurations, etc.
[0077] Examples, as described herein, may include, or may operate
by, logic or a number of components, modules, or mechanisms.
Modules are tangible entities (e.g., hardware) capable of
performing specified operations and may be configured or arranged
in a certain manner. In an example, circuits may be arranged (e.g.,
internally or with respect to external entities such as other
circuits) in a specified manner as a module. In an example, the
whole or part of one or more computer systems (e.g., a standalone,
client or server computer system) or one or more hardware
processors may be configured by firmware or software (e.g.,
instructions, an application portion, or an application) as a
module that operates to perform specified operations. In an
example, the software may reside on a machine-readable medium. In
an example, the software, when executed by the underlying hardware
of the module, causes the hardware to perform the specified
operations.
[0078] Accordingly, the term "module" is understood to encompass a
tangible entity, be that an entity that is physically constructed,
specifically configured (e.g., hardwired), or temporarily (e.g.,
transitorily) configured (e.g., programmed) to operate in a
specified manner or to perform part or all of any operation
described herein. Considering examples in which modules are
temporarily configured, each of the modules need not be
instantiated at any one moment in time. For example, where the
modules comprise a general-purpose hardware processor configured
using software, the general-purpose hardware processor may be
configured as respective different modules at different times.
Software may accordingly configure a hardware processor, for
example, to constitute a particular module at one instance of time
and to constitute a different module at a different instance of
time.
[0079] Machine (e.g., computer system) 500 may include a hardware
processor 502 (e.g., a central processing unit (CPU), a graphics
processing unit (GPU), a hardware processor core, or any
combination thereof), a main memory 504 and a static memory 506,
some or all of which may communicate with each other via an
interlink (e.g., bus) 508. The machine 500 may further include a
display unit 510, an alphanumeric input device 512 (e.g., a
keyboard), and a user interface (UI) navigation device 514 (e.g., a
mouse). In an example, the display unit 510, input device 512 and
UI navigation device 514 may be a touch screen display. The machine
500 may additionally include a storage device (e.g., drive unit)
516, a signal generation device 518 (e.g., a speaker), a network
interface device 520, and one or more sensors 521, such as a global
positioning system (GPS) sensor, compass, accelerometer, or other
sensor. The machine 500 may include an output controller 528, such
as a serial (e.g., USB, parallel, or other wired or wireless (e.g.,
infrared (IR), near field communication (NFC), etc.) connection to
communicate or control one or more peripheral devices (e.g., a
printer, card reader, etc.)
[0080] The storage device 516 may include a machine-readable medium
522 on which is stored one or more sets of data structures or
instructions 524 (e.g., software) embodying or utilized by any one
or more of the techniques or functions described herein. The
instructions 524 may also reside, completely or at least partially,
within the main memory 504, within static memory 506, or within the
hardware processor 502 during execution thereof by the machine 500.
In an example, one or any combination of the hardware processor
502, the main memory 504, the static memory 506, or the storage
device 516 may constitute machine-readable media.
[0081] Although the machine-readable medium 522 is illustrated as a
single medium, the term "machine-readable medium" may include a
single medium or multiple media (e.g., a centralized or distributed
database, and/or associated caches and servers) configured to store
the one or more instructions 524.
[0082] The term "machine-readable medium" may include any medium
that is capable of storing, encoding, or carrying instructions for
execution by the machine 500 and that cause the machine 500 to
perform any one or more of the techniques of the present
disclosure, or that is capable of storing, encoding or carrying
data structures used by or associated with such instructions.
Non-limiting machine-readable medium examples may include
solid-state memories, and optical and magnetic media. Accordingly,
machine-readable media are not transitory propagating signals.
Specific examples of machine-readable media may include
non-volatile memory, such as semiconductor memory devices (e.g.,
Electrically Programmable Read-Only Memory (EPROM), Electrically
Erasable Programmable Read-Only Memory (EEPROM)) and flash memory
devices; magnetic disks, such as internal hard disks and removable
disks; magneto-optical disks; Random Access Memory (RAM); Solid
State Drives (SSD); and CD-ROM and DVD-ROM disks.
[0083] The instructions 524 may further be transmitted or received
over a communications network 526 using a transmission medium via
the network interface device 520 utilizing any one of a number of
transfer protocols (e.g., frame relay, Internet protocol (IP),
transmission control protocol (TCP), user datagram protocol (UDP),
hypertext transfer protocol (HTTP), etc.). Example communication
networks may include a local area network (LAN), a wide area
network (WAN), a packet data network (e.g., the Internet), mobile
telephone networks (e.g., cellular networks), Plain Old Telephone
(POTS) networks, and wireless data networks (e.g., Institute of
Electrical and Electronics Engineers (IEEE) 802.11 family of
standards known as Wi-Fi.RTM., IEEE 802.16 family of standards
known as WiMAX.RTM.), IEEE 802.15.4 family of standards,
Bluetooth.RTM., Bluetooth.RTM. low energy technology, ZigBee.RTM.,
peer-to-peer (P2P) networks, among others. In an example, the
network interface device 520 may include one or more physical jacks
(e.g., Ethernet, coaxial, or phone jacks) or one or more antennas
to connect to the communications network 526. In an example, the
network interface device 520 may include a plurality of antennas to
wirelessly communicate using at least one of single-input
multiple-output (SIMO), multiple-input multiple-output (MIMO), or
multiple-input single-output (MISO) techniques. The term
"transmission medium" shall be taken to include any intangible
medium that is capable of storing, encoding, or carrying
instructions for execution by the machine 500, and includes digital
or analog communications signals or other intangible medium to
facilitate communication of such software.
[0084] Conventional terms in the fields of computer systems and
computer networking have been used herein. The terms are known in
the art and are provided only as a non-limiting example for
convenience purposes. Accordingly, the interpretation of the
corresponding terms in the claims, unless stated otherwise, is not
limited to any particular definition. Although specific embodiments
have been illustrated and described herein, it will be appreciated
by those of ordinary skill in the art that any arrangement that is
calculated to achieve the same purpose may be substituted for the
specific embodiments shown. Many adaptations will be apparent to
those of ordinary skill in the art. Accordingly, this application
is intended to cover any adaptations or variations.
[0085] The above detailed description includes references to the
accompanying drawings, which form a part of the detailed
description. The drawings show, by way of illustration, specific
embodiments that may be practiced. These embodiments are also
referred to herein as "examples." Such examples may include
elements in addition to those shown or described. However, the
present inventors also contemplate examples in which only those
elements shown or described are provided. Moreover, the present
inventors also contemplate examples using any combination or
permutation of those elements shown or described (or one or more
aspects thereof), either with respect to a particular example (or
one or more aspects thereof), or with respect to other examples (or
one or more aspects thereof) shown or described herein.
[0086] In this document, the terms "a" or "an" are used, as is
common in patent documents, to include one or more than one,
independent of any other instances or usages of "at least one" or
"one or more." In this document, the term "or" is used to refer to
a nonexclusive or, such that "A or B" includes "A but not B," "B
but not A," and "A and B," unless otherwise indicated. Moreover, in
the following claims, the terms "first," "second," and "third,"
etc. are used merely as labels, and are not intended to impose
numerical requirements on their objects. In this document, a sensor
set may include one or more sensors, which may be of different
types. Furthermore, two different sensor sets may include one or
more sensors that belong to both sensor sets.
[0087] In this Detailed Description, various features may have been
grouped together to streamline the disclosure. This should not be
interpreted as intending that an unclaimed disclosed feature is
essential to any claim. Rather, inventive subject matter may lie in
less than all features of a particular disclosed embodiment.
[0088] The above description is intended to be illustrative, and
not restrictive. For example, the above-described examples (or one
or more aspects thereof) may be used in combination with each
other. Other embodiments may be used, such as by a person of
ordinary skill in the art upon reviewing the above description.
[0089] Various non-limiting embodiments have been described. It
will be appreciated that suitable alternatives are possible without
departing from the scope of the examples described herein. These
and other examples are within the scope of the following
claims.
* * * * *